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<li class="navelem"><a class="el" href="dir_d44c64559bbebec7f509842c48db8b23.html">include</a></li><li class="navelem"><a class="el" href="dir_9d0c4981f10d03078bcfd5c74fe41ce8.html">shark</a></li><li class="navelem"><a class="el" href="dir_24fc231769ada4cfc8add7cd238ad0f8.html">Algorithms</a></li><li class="navelem"><a class="el" href="dir_a8795d52992905c0ec88467e5ad28556.html">DirectSearch</a></li>  </ul>
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<a href="_grid_search_8h.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="comment">//===========================================================================</span><span class="comment"></span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="comment">/*!</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="comment"> *</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="comment"> *</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="comment"> * \brief       GridSearch.h</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="comment"> *</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="comment"> *</span></div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="comment"> *</span></div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="comment"> * \author      O. Krause</span></div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span><span class="comment"> * \date        2010</span></div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span><span class="comment"> *</span></div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment"> *</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment"> * \par Copyright 1995-2017 Shark Development Team</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="comment"> *</span></div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span><span class="comment"> * &lt;BR&gt;&lt;HR&gt;</span></div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="comment"> * This file is part of Shark.</span></div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span><span class="comment"> * &lt;https://shark-ml.github.io/Shark/&gt;</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="comment"> *</span></div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span><span class="comment"> * Shark is free software: you can redistribute it and/or modify</span></div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span><span class="comment"> * it under the terms of the GNU Lesser General Public License as published</span></div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span><span class="comment"> * by the Free Software Foundation, either version 3 of the License, or</span></div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span><span class="comment"> * (at your option) any later version.</span></div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span><span class="comment"> *</span></div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span><span class="comment"> * Shark is distributed in the hope that it will be useful,</span></div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span><span class="comment"> * but WITHOUT ANY WARRANTY; without even the implied warranty of</span></div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span><span class="comment"> * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the</span></div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span><span class="comment"> * GNU Lesser General Public License for more details.</span></div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span><span class="comment"> *</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span><span class="comment"> * You should have received a copy of the GNU Lesser General Public License</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span><span class="comment"> * along with Shark.  If not, see &lt;http://www.gnu.org/licenses/&gt;.</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span><span class="comment"> *</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span><span class="comment"> */</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span><span class="comment">//===========================================================================</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span> </div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span><span class="preprocessor">#ifndef SHARK_ALGORITHMS_GRIDSEARCH_H</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span><span class="preprocessor">#define SHARK_ALGORITHMS_GRIDSEARCH_H</span></div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span> </div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span><span class="preprocessor">#include &lt;<a class="code" href="_abstract_single_objective_optimizer_8h.html">shark/Algorithms/AbstractSingleObjectiveOptimizer.h</a>&gt;</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span><span class="preprocessor">#include &lt;<a class="code" href="_random_8h.html">shark/Core/Random.h</a>&gt;</span></div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span> </div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span><span class="preprocessor">#include &lt;boost/serialization/vector.hpp&gt;</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span> </div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span> </div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a> {</div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span><span class="comment"></span> </div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span><span class="comment">/// </span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span><span class="comment">///  \brief Optimize by trying out a grid of configurations</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span><span class="comment">/// </span></div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span><span class="comment">///  \par</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span><span class="comment">///  The GridSearch class allows for the definition of a grid in</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span><span class="comment">///  parameter space. It does a simple one-step optimization over</span></div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span><span class="comment">///  the grid by trying out every possible parameter combination.</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span><span class="comment">///  Please note that the computation effort grows exponentially</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span><span class="comment">///  with the number of parameters.</span></div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span><span class="comment">/// </span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span><span class="comment">///  \par</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span><span class="comment">///  If you only want to try a subset of the grid, consider using</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span><span class="comment">///  the PointSearch class instead.</span></div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span><span class="comment">///  A more sophisticated (less exhaustive) grid search variant is</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span><span class="comment">///  available with the NestedGridSearch class.</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span><span class="comment">/// \ingroup singledirect</span></div>
<div class="foldopen" id="foldopen00063" data-start="{" data-end="};">
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html">   63</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_grid_search.html" title="Optimize by trying out a grid of configurations.">GridSearch</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>{</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span><span class="keyword">public</span>:</div>
<div class="foldopen" id="foldopen00066" data-start="{" data-end="}">
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a165ed5b1d76b1946e377b454f1cf8477">   66</a></span>    <a class="code hl_function" href="classshark_1_1_grid_search.html#a165ed5b1d76b1946e377b454f1cf8477">GridSearch</a>() {</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>=<span class="keyword">false</span>;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>    }</div>
</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span><span class="comment"></span> </div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00071" data-start="{" data-end="}">
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a8cc649ca8e82a43be3241188771caae8">   71</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_grid_search.html#a8cc649ca8e82a43be3241188771caae8" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno">   72</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>        <span class="keywordflow">return</span> <span class="stringliteral">&quot;GridSearch&quot;</span>;</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>    }</div>
</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span><span class="comment"></span> </div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span><span class="comment">    ///  uniform initialization for all parameters</span></div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span><span class="comment">    ///  \param  params  number of model parameters to optimize</span></div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span><span class="comment">    ///  \param  min     smallest parameter value</span></div>
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno">   79</span><span class="comment">    ///  \param  max     largest parameter value</span></div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span><span class="comment">    ///  \param  numSections   total number of values in the interval</span></div>
<div class="foldopen" id="foldopen00081" data-start="{" data-end="}">
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#abb3472e263958e425bb45b3556d52e7c">   81</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#abb3472e263958e425bb45b3556d52e7c">configure</a>(<span class="keywordtype">size_t</span> params, <span class="keywordtype">double</span> min, <span class="keywordtype">double</span> max, <span class="keywordtype">size_t</span> numSections)</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>    {</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(params&gt;=1);</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(min&lt;=max);</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(numSections&gt;=1);</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.resize(params);</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> i = 0; i &lt; numSections; i++)</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>        {</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            <span class="keywordtype">double</span> section = min + i * (max - min) / (numSections - 1.0);</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>            <span class="keywordflow">for</span>(<span class="keyword">auto</span>&amp; node: <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>)</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>                node.push_back(section);</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>        }</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span>    }</div>
</div>
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno">   95</span><span class="comment"></span> </div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span><span class="comment">    ///  individual definition for every parameter</span></div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span><span class="comment">    ///  \param  min     smallest value for every parameter</span></div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span><span class="comment">    ///  \param  max     largest value for every parameter</span></div>
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno">   99</span><span class="comment">    ///  \param  sections   total number of values for every parameter</span></div>
<div class="foldopen" id="foldopen00100" data-start="{" data-end="}">
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a8af5358813f9b5c6dc126ebaa70c55a8">  100</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#a8af5358813f9b5c6dc126ebaa70c55a8">configure</a>(<span class="keyword">const</span> std::vector&lt;double&gt;&amp; min, <span class="keyword">const</span> std::vector&lt;double&gt;&amp; max, <span class="keyword">const</span> std::vector&lt;size_t&gt;&amp; sections)</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>    {</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>        <span class="keywordtype">size_t</span> params = min.size();</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(sections.size() == params);</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(max.size() == params);</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(min &lt;= max);</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span> </div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.resize(params);</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> dimension = 0; dimension &lt; params; dimension++)</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>        {</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>            <span class="keywordtype">size_t</span> numSections = sections[dimension];</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>            std::vector&lt;double&gt;&amp; node = <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[dimension];</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>            node.resize(numSections);</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span> </div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>            <span class="keywordflow">if</span> ( numSections == 1 )</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>            {</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>                node[0] = (( min[dimension] + max[dimension] ) / 2.0);</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>            }</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>            <span class="keywordflow">else</span> <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> section = 0; section &lt; numSections; section++)</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>                {</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>                    node[section] = (min[dimension] + section * (max[dimension] - min[dimension]) / (numSections - 1.0));</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>                }</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>        }</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>    }</div>
</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span> </div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span><span class="comment"></span> </div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span><span class="comment">    ///  special case for 2D grid, individual definition for every parameter</span></div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span><span class="comment">    ///  \param  min1     smallest value for first parameter</span></div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span><span class="comment">    ///  \param  max1     largest value for first parameter</span></div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span><span class="comment">    ///  \param  sections1   total number of values for first parameter</span></div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span><span class="comment">    ///  \param  min2     smallest value for second parameter</span></div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span><span class="comment">    ///  \param  max2     largest value for second parameter</span></div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span><span class="comment">    ///  \param  sections2   total number of values for second parameter</span></div>
<div class="foldopen" id="foldopen00134" data-start="{" data-end="}">
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#ad10a520a0811e4926f8830c42cd5b994">  134</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#ad10a520a0811e4926f8830c42cd5b994">configure</a>(<span class="keywordtype">double</span> min1, <span class="keywordtype">double</span> max1, <span class="keywordtype">size_t</span> sections1, <span class="keywordtype">double</span> min2, <span class="keywordtype">double</span> max2, <span class="keywordtype">size_t</span> sections2)</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>    {</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(min1&lt;=max1);</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(min2&lt;=max2);</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(sections1 &gt; 0);</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(sections2 &gt; 0);</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span> </div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.resize(2u);</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span> </div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>        <span class="keywordflow">if</span> ( sections1 == 1 ) {</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>            <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[0].push_back(( min1 + max1 ) / 2.0);</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>        } <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> section = 0; section &lt; sections1; section++)</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span>                <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[0].push_back(min1 + section * (max1 - min1) / (sections1 - 1.0));</div>
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno">  148</span>        }</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span> </div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>        <span class="keywordflow">if</span> ( sections2 == 1 ) {</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>            <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[1].push_back(( min2 + max2 ) / 2.0);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>        } <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> section = 0; section &lt; sections2; section++)</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>                <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[1].push_back(min2 + section * (max2 - min2) / (sections2 - 1.0));</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>        }</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>    }</div>
</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span><span class="comment"></span> </div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span><span class="comment">    ///  special case for line search</span></div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span><span class="comment">    ///  \param  min1     smallest value for first parameter</span></div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span><span class="comment">    ///  \param  max1     largest value for first parameter</span></div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span><span class="comment">    ///  \param  sections1   total number of values for first parameter</span></div>
<div class="foldopen" id="foldopen00162" data-start="{" data-end="}">
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a8529244e3398f7d97136436d5edad43b">  162</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#a8529244e3398f7d97136436d5edad43b">configure</a>(<span class="keywordtype">double</span> min1, <span class="keywordtype">double</span> max1, <span class="keywordtype">size_t</span> sections1)</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>    {</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(min1&lt;=max1);</div>
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno">  165</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(sections1 &gt; 0);</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span> </div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.resize(1u);</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span> </div>
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno">  169</span>        <span class="keywordflow">if</span> ( sections1 == 1 ) {</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>            <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[0].push_back(( min1 + max1 ) / 2.0);</div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>        } <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> section = 0; section &lt; sections1; section++)</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>                <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[0].push_back(min1 + section * (max1 - min1) / (sections1 - 1.0));</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>        }</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>    }</div>
</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span> </div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span><span class="comment"></span> </div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span><span class="comment">    ///  uniform definition of the values to test for all parameters</span></div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span><span class="comment">    ///  \param  params  number of model parameters to optimize</span></div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span><span class="comment">    ///  \param  values  values used for every coordinate</span></div>
<div class="foldopen" id="foldopen00181" data-start="{" data-end="}">
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a9f91e41fa7df5ccbf0a5d05747933a1c">  181</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#a9f91e41fa7df5ccbf0a5d05747933a1c">configure</a>(<span class="keywordtype">size_t</span> params, <span class="keyword">const</span> std::vector&lt;double&gt;&amp; values)</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>    {</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(params &gt; 0);</div>
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno">  184</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(values.size() &gt; 0);</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.resize(params);</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>        <span class="keywordflow">for</span>(<span class="keyword">auto</span>&amp; node: <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>)</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>            node=values;</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>    }</div>
</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span><span class="comment"></span> </div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span><span class="comment">    ///  individual definition for every parameter</span></div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span><span class="comment">    ///  \param  values  values used. The first dimension is the parameter, the second dimension is the node.</span></div>
<div class="foldopen" id="foldopen00193" data-start="{" data-end="}">
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a44d3092775b93a0a8a88f756501e3250">  193</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#a44d3092775b93a0a8a88f756501e3250">configure</a>(<span class="keyword">const</span> std::vector&lt;std::vector&lt;double&gt; &gt;&amp; values)</div>
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno">  194</span>    {</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(values.size() &gt; 0);</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a> = values;</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>    }</div>
</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span> </div>
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno">  200</span>    <span class="comment">//from ISerializable</span></div>
<div class="foldopen" id="foldopen00201" data-start="{" data-end="}">
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a8fa6193d9d0509929c827c59b62e3b3a">  201</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#a8fa6193d9d0509929c827c59b62e3b3a" title="Read the component from the supplied archive.">read</a>( <a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a> &amp; archive )</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>    {</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>;</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>;</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point;</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value;</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span>    }</div>
</div>
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno">  208</span> </div>
<div class="foldopen" id="foldopen00209" data-start="{" data-end="}">
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a3b6f1136e80165ecdea7e03df801f96f">  209</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#a3b6f1136e80165ecdea7e03df801f96f" title="Write the component to the supplied archive.">write</a>( <a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a> &amp; archive )<span class="keyword"> const</span></div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>;</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>;</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point;</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value;</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>    }</div>
</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span><span class="comment"></span> </div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span><span class="comment">    /*! If Gridsearch wasn&#39;t configured before calling this method, it is default constructed</span></div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span><span class="comment">     *  as a net spanning the range [-1,1] in all dimensions with 5 searchpoints (-1,-0.5,0,0.5,1).</span></div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span><span class="comment">     *  so don&#39;t forget to scale the parameter-ranges of the objective function!</span></div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span><span class="comment">     *  The startingPoint can actually be anything, only its dimension has to be correct.</span></div>
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno">  221</span><span class="comment">     */</span></div>
<div class="foldopen" id="foldopen00222" data-start="{" data-end="}">
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#aee57c542b7ca10f41028da93434bf316">  222</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#aee57c542b7ca10f41028da93434bf316">init</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; startingPoint) {</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>        <a class="code hl_function" href="classshark_1_1_abstract_optimizer.html#ae7a23300641448c761b6aa0305b7ef66" title="Convenience function that checks whether the features of the supplied objective function match with t...">checkFeatures</a>(objectiveFunction);</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span> </div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>        <span class="keywordflow">if</span>(!<a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>)</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span>            <a class="code hl_function" href="classshark_1_1_grid_search.html#abb3472e263958e425bb45b3556d52e7c">configure</a>(startingPoint.size(),-1,1,5);</div>
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno">  227</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(startingPoint.size() == <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.size());</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point=startingPoint;</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>    }</div>
</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;<a class="code hl_function" href="classshark_1_1_grid_search.html#aee57c542b7ca10f41028da93434bf316">::init</a>;<span class="comment"></span></div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span><span class="comment">    /*! Assign linearly progressing grid values to one certain parameter only.</span></div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span><span class="comment">     *  This is especially useful if one parameter needs special treatment</span></div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span><span class="comment">     *  \param index the index of the parameter to which grid values are assigned</span></div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span><span class="comment">     *  \param noOfSections how many grid points should be assigned to that parameter</span></div>
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno">  235</span><span class="comment">     *  \param min smallest value for that parameter</span></div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span><span class="comment">     *  \param max largest value for that parameter */</span></div>
<div class="foldopen" id="foldopen00237" data-start="{" data-end="}">
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a13c9339693b4c33f25a1842ca09aaea3">  237</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#a13c9339693b4c33f25a1842ca09aaea3">assignLinearRange</a>(<span class="keywordtype">size_t</span> index, <span class="keywordtype">size_t</span> noOfSections, <span class="keywordtype">double</span> min, <span class="keywordtype">double</span> max)</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>    {</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>( noOfSections &gt;= 1);</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>( min &lt;= max );</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>( index &lt; <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.size() );</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span> </div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[index].clear();</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span>        <span class="keywordflow">if</span> ( noOfSections == 1 ) {</div>
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno">  245</span>            <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[index].push_back(( min+max) / 2.0);</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>        }</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>        <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>            <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[index].reserve(noOfSections);</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> section = 0; section &lt; noOfSections; section++)</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span>                <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[index].push_back(min + section*( max-min ) / ( noOfSections-1.0 ));</div>
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno">  251</span>        }</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>    }</div>
</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span><span class="comment"></span> </div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span><span class="comment">    /*! Set exponentially progressing grid values for one certain parameter only.</span></div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span><span class="comment">     *  This is especially useful if one parameter needs special treatment. The</span></div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span><span class="comment">     *  grid points will be filled with values \f$ factor \cdot expbase ^i \f$,</span></div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span><span class="comment">     *  where i does integer steps between min and max.</span></div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span><span class="comment">     *  \param index the index of the parameter that gets new grid values</span></div>
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno">  259</span><span class="comment">     *  \param factor the value that the exponential base grid should be multiplied by</span></div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span><span class="comment">     *  \param exp_base the exponential grid will progress on this base (e.g. 2, 10)</span></div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span><span class="comment">     *  \param min the smallest exponent for exp_base</span></div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span><span class="comment">     *  \param max the largest exponent for exp_base  */</span></div>
<div class="foldopen" id="foldopen00263" data-start="{" data-end="}">
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a9f7f3ace7fa5ad50b430ee33104e5fbc">  263</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#a9f7f3ace7fa5ad50b430ee33104e5fbc">assignExponentialRange</a>(<span class="keywordtype">size_t</span> index, <span class="keywordtype">double</span> factor, <span class="keywordtype">double</span> exp_base, <span class="keywordtype">int</span> min, <span class="keywordtype">int</span> max)</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>    {</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>( min &lt;= max );</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>( index &lt; <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.size() );</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span> </div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[index].clear();</div>
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno">  269</span>        <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[index].reserve(max-min);</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>        <span class="keywordflow">for</span> (<span class="keywordtype">int</span> section = 0; section &lt;= (max-min); section++)</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>            <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[index].push_back( factor * std::pow( exp_base, section+min ));</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>    }</div>
</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span><span class="comment"></span> </div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span><span class="comment">    ///  Please note that for the grid search optimizer it does</span></div>
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno">  275</span><span class="comment">    ///  not make sense to call step more than once, as the</span></div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span><span class="comment">    ///  solution does not improve iteratively.</span></div>
<div class="foldopen" id="foldopen00277" data-start="{" data-end="}">
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#afb6458a381e6b45cb8290289a573d642">  277</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_grid_search.html#afb6458a381e6b45cb8290289a573d642">step</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction) {</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>        <span class="keywordtype">size_t</span> dimensions = <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>.size();</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>        std::vector&lt;size_t&gt; index(dimensions, 0);</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value = 1e100;</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>        RealVector point(dimensions);</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span> </div>
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno">  283</span>        <span class="comment">// loop through all grid points</span></div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>        <span class="keywordflow">while</span> (<span class="keyword">true</span>)</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>        {</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>            <span class="comment">// define the parameters</span></div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> dimension = 0; dimension &lt; dimensions; dimension++)</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>                point(dimension) = <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[dimension][index[dimension]];</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span> </div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>            <span class="comment">// evaluate the model</span></div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>            <span class="keywordflow">if</span> (objectiveFunction.isFeasible(point))</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>            {</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>                <span class="keywordtype">double</span> error = objectiveFunction.eval(point);</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span> </div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span><span class="preprocessor">#ifdef SHARK_CV_VERBOSE_1</span></div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>                std::cout &lt;&lt; <span class="stringliteral">&quot;.&quot;</span> &lt;&lt; std::flush;</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span><span class="preprocessor">#endif</span></div>
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno">  298</span><span class="preprocessor">#ifdef SHARK_CV_VERBOSE</span></div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>                std::cout &lt;&lt; point &lt;&lt; <span class="stringliteral">&quot;\t&quot;</span> &lt;&lt; error &lt;&lt; std::endl;</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span><span class="preprocessor">#endif</span></div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span>                <span class="keywordflow">if</span> (error &lt; <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value)</div>
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno">  302</span>                {</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>                    <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value = error;</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>                    <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point = point;       <span class="comment">// [TG] swap() solution is out, caused ugly memory bug, I changed this back</span></div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>                }</div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span>            }</div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span> </div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>            <span class="comment">// next index</span></div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>            <span class="keywordtype">size_t</span> dimension = 0;</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>            <span class="keywordflow">for</span> (; dimension &lt; dimensions; dimension++)</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>            {</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>                index[dimension]++;</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>                <span class="keywordflow">if</span> (index[dimension] &lt; <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>[dimension].size()) <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>                index[dimension] = 0;</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>            }</div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>            <span class="keywordflow">if</span> (dimension == dimensions) <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>        }</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span><span class="preprocessor">#ifdef SHARK_CV_VERBOSE_1</span></div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>        std::cout &lt;&lt; std::endl;</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span><span class="preprocessor">#endif</span></div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>    }</div>
</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span> </div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span><span class="keyword">protected</span>:</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span><span class="comment"></span> </div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span><span class="comment">    ///  The array columns contain the grid values for the corresponding parameter axis.</span></div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5">  326</a></span><span class="comment"></span>    std::vector&lt;std::vector&lt;double&gt; &gt; <a class="code hl_variable" href="classshark_1_1_grid_search.html#a940a5bb29971527ff6b0dc10638e13f5" title="The array columns contain the grid values for the corresponding parameter axis.">m_nodeValues</a>;</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span> </div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno"><a class="line" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">  328</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_grid_search.html#a36538b13ff96f301f26a0c96e222e5cd">m_configured</a>;</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>};</div>
</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span> </div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span><span class="comment"></span> </div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span><span class="comment">/// </span></div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span><span class="comment">///  \brief Nested grid search</span></div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span><span class="comment">/// </span></div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span><span class="comment">///  \par</span></div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span><span class="comment">///  The NestedGridSearch class is an iterative optimizer,</span></div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span><span class="comment">///  doing one grid search in every iteration. In every</span></div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span><span class="comment">///  iteration, it halves the grid extent doubling the</span></div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span><span class="comment">///  resolution in every coordinate.</span></div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span><span class="comment">/// </span></div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span><span class="comment">///  \par</span></div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span><span class="comment">///  Although nested grid search is much less exhaustive</span></div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span><span class="comment">///  than standard grid search, it still suffers from</span></div>
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno">  344</span><span class="comment">///  exponential time and memory complexity in the number</span></div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span><span class="comment">///  of variables optimized. Therefore, if the number of</span></div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span><span class="comment">///  variables is larger than 2 or 3, consider using the</span></div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span><span class="comment">///  CMA instead.</span></div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span><span class="comment">/// </span></div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span><span class="comment">///  \par</span></div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span><span class="comment">///  Nested grid search works as follows: The optimizer</span></div>
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno">  351</span><span class="comment">///  defined a 5x5x...x5 equi-distant grid (depending on</span></div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span><span class="comment">///  the search space dimension) on an initially defined</span></div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span><span class="comment">///  search cube. During every grid search iteration,</span></div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span><span class="comment">///  the error is computed for all  grid points.</span></div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span><span class="comment">/// Then the grid is moved</span></div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span><span class="comment">///  to the best grid point found so far and contracted</span></div>
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno">  357</span><span class="comment">///  by a factor of two in each dimension. Each call to</span></div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span><span class="comment">///  the optimize() function performs one such step.</span></div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno">  359</span><span class="comment">/// </span></div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span><span class="comment">///  \par</span></div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span><span class="comment">///  Let N denote the number of parameters to optimize.</span></div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span><span class="comment">///  To compute the error landscape at the current</span></div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span><span class="comment">///  zoom level, the algorithm has to do \f$ 5^N \f$</span></div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span><span class="comment">///  error function evaluations in every iteration.</span></div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span><span class="comment">/// </span></div>
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno">  366</span><span class="comment">///  \par</span></div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span><span class="comment">///  The grid is always centered around the best</span></div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span><span class="comment">///  solution currently known. If this solution is</span></div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span><span class="comment">///  located at the boundary, the landscape may exceed</span></div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span><span class="comment">///  the parameter range defined m_minimum and m_maximum.</span></div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span><span class="comment">///  These invalid landscape values are not used.</span></div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span><span class="comment">/// </span></div>
<div class="foldopen" id="foldopen00373" data-start="{" data-end="};">
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html">  373</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_nested_grid_search.html" title="Nested grid search.">NestedGridSearch</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;</div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span>{</div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span><span class="comment">    ///  Constructor</span></div>
<div class="foldopen" id="foldopen00377" data-start="{" data-end="}">
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#af843acc075e013fe5d75ffc0e5135e10">  377</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#af843acc075e013fe5d75ffc0e5135e10" title="Constructor.">NestedGridSearch</a>()</div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span>    {</div>
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno">  379</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#aba7c13a5f426918e4e64760d7e5b3624">m_configured</a>=<span class="keyword">false</span>;</div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>    }</div>
</div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span><span class="comment"></span> </div>
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno">  382</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00383" data-start="{" data-end="}">
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#a1a0f8c2203e464572921d4c733b7e642">  383</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a1a0f8c2203e464572921d4c733b7e642" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno">  385</span>        <span class="keywordflow">return</span> <span class="stringliteral">&quot;NestedGridSearch&quot;</span>;</div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span>    }</div>
</div>
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno">  387</span><span class="comment"></span> </div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span><span class="comment">    /// </span></div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span><span class="comment">    ///  \brief Initialization of the nested grid search.</span></div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno">  390</span><span class="comment">    /// </span></div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span><span class="comment">    ///  \par</span></div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span><span class="comment">    ///  The min and max arrays define ranges for every parameter to optimize.</span></div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno">  393</span><span class="comment">    ///  These ranges are strict, that is, the algorithm will not try values</span></div>
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno">  394</span><span class="comment">    ///  beyond the range, even if is finds a boundary minimum.</span></div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span><span class="comment">    /// </span></div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno">  396</span><span class="comment">    ///  \param  min    lower end of the parameter range</span></div>
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno">  397</span><span class="comment">    ///  \param  max    upper end of the parameter range</span></div>
<div class="foldopen" id="foldopen00398" data-start="{" data-end="}">
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#a6794001d6de702afc1cb5eb323e1335b">  398</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a6794001d6de702afc1cb5eb323e1335b" title="Initialization of the nested grid search.">configure</a>(<span class="keyword">const</span> std::vector&lt;double&gt;&amp; min, <span class="keyword">const</span> std::vector&lt;double&gt;&amp; max)</div>
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno">  399</span>    {</div>
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno">  400</span>        <span class="keywordtype">size_t</span> dimensions = min.size();</div>
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno">  401</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(max.size() == dimensions);</div>
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno">  402</span> </div>
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno">  403</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a5a473052d0b073aadc27760d91a562cd" title="minimum parameter value to check">m_minimum</a> = min;</div>
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno">  404</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#acf852729d6bcc8ed07b07b536c216b8f" title="maximum parameter value to check">m_maximum</a> = max;</div>
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno">  405</span> </div>
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno">  406</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>.resize(dimensions);</div>
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno">  407</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point.resize(dimensions);</div>
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno">  408</span>        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> dimension = 0; dimension &lt; dimensions; dimension++)</div>
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno">  409</span>        {</div>
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno">  410</span>            <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point(dimension)=0.5 *(min[dimension] + max[dimension]);</div>
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno">  411</span>            <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>[dimension] = 0.25 * (max[dimension] - min[dimension]);</div>
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno">  412</span>        }</div>
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno">  413</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#aba7c13a5f426918e4e64760d7e5b3624">m_configured</a>=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno">  414</span>    }</div>
</div>
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno">  415</span><span class="comment"></span> </div>
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno">  416</span><span class="comment">    /// </span></div>
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno">  417</span><span class="comment">    ///  \brief Initialization of the nested grid search.</span></div>
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno">  418</span><span class="comment">    /// </span></div>
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno">  419</span><span class="comment">    ///  \par</span></div>
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno">  420</span><span class="comment">    ///  The min and max values define ranges for every parameter to optimize.</span></div>
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno">  421</span><span class="comment">    ///  These ranges are strict, that is, the algorithm will not try values</span></div>
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno">  422</span><span class="comment">    ///  beyond the range, even if is finds a boundary minimum.</span></div>
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno">  423</span><span class="comment">    /// </span></div>
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno">  424</span><span class="comment">    ///  \param parameters number of parameters to optimize</span></div>
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno">  425</span><span class="comment">    ///  \param  min    lower end of the parameter range</span></div>
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno">  426</span><span class="comment">    ///  \param  max    upper end of the parameter range</span></div>
<div class="foldopen" id="foldopen00427" data-start="{" data-end="}">
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#a18b11da83f808e86b04c70c51e716678">  427</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a18b11da83f808e86b04c70c51e716678" title="Initialization of the nested grid search.">configure</a>(<span class="keywordtype">size_t</span> parameters, <span class="keywordtype">double</span> min, <span class="keywordtype">double</span> max)</div>
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno">  428</span>    {</div>
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno">  429</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(parameters &gt; 0);</div>
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno">  430</span> </div>
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno">  431</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a5a473052d0b073aadc27760d91a562cd" title="minimum parameter value to check">m_minimum</a>=std::vector&lt;double&gt;(parameters,min);</div>
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno">  432</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#acf852729d6bcc8ed07b07b536c216b8f" title="maximum parameter value to check">m_maximum</a>=std::vector&lt;double&gt;(parameters,max);</div>
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno">  433</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>=std::vector&lt;double&gt;(parameters,0.25 * (max - min));</div>
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno">  434</span> </div>
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno">  435</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point.resize(parameters);</div>
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno">  436</span> </div>
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno">  437</span>        <span class="keywordtype">double</span> start=0.5 *(min + max);</div>
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno">  438</span>        <span class="keywordflow">for</span>(<span class="keywordtype">double</span>&amp; value: <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point)</div>
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno">  439</span>            value=start;</div>
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno">  440</span>        <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#aba7c13a5f426918e4e64760d7e5b3624">m_configured</a>=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno">  441</span>    }</div>
</div>
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno">  442</span> </div>
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno">  443</span>    <span class="comment">//from ISerializable</span></div>
<div class="foldopen" id="foldopen00444" data-start="{" data-end="}">
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#acc163d14a00c8801b118dc6e48f1bb44">  444</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#acc163d14a00c8801b118dc6e48f1bb44" title="Read the component from the supplied archive.">read</a>( <a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a> &amp; archive )</div>
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno">  445</span>    {</div>
<div class="line"><a id="l00446" name="l00446"></a><span class="lineno">  446</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a5a473052d0b073aadc27760d91a562cd" title="minimum parameter value to check">m_minimum</a>;</div>
<div class="line"><a id="l00447" name="l00447"></a><span class="lineno">  447</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#acf852729d6bcc8ed07b07b536c216b8f" title="maximum parameter value to check">m_maximum</a>;</div>
<div class="line"><a id="l00448" name="l00448"></a><span class="lineno">  448</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>;</div>
<div class="line"><a id="l00449" name="l00449"></a><span class="lineno">  449</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#aba7c13a5f426918e4e64760d7e5b3624">m_configured</a>;</div>
<div class="line"><a id="l00450" name="l00450"></a><span class="lineno">  450</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point;</div>
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno">  451</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value;</div>
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno">  452</span>    }</div>
</div>
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno">  453</span> </div>
<div class="foldopen" id="foldopen00454" data-start="{" data-end="}">
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#aa9b5d9cd975a160a3f24edae703bf1e7">  454</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#aa9b5d9cd975a160a3f24edae703bf1e7" title="Write the component to the supplied archive.">write</a>( <a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a> &amp; archive )<span class="keyword"> const</span></div>
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno">  455</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno">  456</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a5a473052d0b073aadc27760d91a562cd" title="minimum parameter value to check">m_minimum</a>;</div>
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno">  457</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#acf852729d6bcc8ed07b07b536c216b8f" title="maximum parameter value to check">m_maximum</a>;</div>
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno">  458</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>;</div>
<div class="line"><a id="l00459" name="l00459"></a><span class="lineno">  459</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#aba7c13a5f426918e4e64760d7e5b3624">m_configured</a>;</div>
<div class="line"><a id="l00460" name="l00460"></a><span class="lineno">  460</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point;</div>
<div class="line"><a id="l00461" name="l00461"></a><span class="lineno">  461</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value;</div>
<div class="line"><a id="l00462" name="l00462"></a><span class="lineno">  462</span>    }</div>
</div>
<div class="line"><a id="l00463" name="l00463"></a><span class="lineno">  463</span><span class="comment"></span> </div>
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno">  464</span><span class="comment">    ///  if NestedGridSearch was not configured before this call, it is default initialized ti the range[-1,1] for every parameter</span></div>
<div class="foldopen" id="foldopen00465" data-start="{" data-end="}">
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#a7d9ffc24f40f1824e0973e880c48d474">  465</a></span><span class="comment"></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a7d9ffc24f40f1824e0973e880c48d474" title="if NestedGridSearch was not configured before this call, it is default initialized ti the range[-1,...">init</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; startingPoint) {</div>
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno">  466</span>        <a class="code hl_function" href="classshark_1_1_abstract_optimizer.html#ae7a23300641448c761b6aa0305b7ef66" title="Convenience function that checks whether the features of the supplied objective function match with t...">checkFeatures</a>(objectiveFunction);</div>
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno">  467</span> </div>
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno">  468</span>        <span class="keywordflow">if</span>(!<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#aba7c13a5f426918e4e64760d7e5b3624">m_configured</a>)</div>
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno">  469</span>            <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a6794001d6de702afc1cb5eb323e1335b" title="Initialization of the nested grid search.">configure</a>(startingPoint.size(),-1,1);</div>
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno">  470</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>.size()==startingPoint.size());</div>
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno">  471</span> </div>
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno">  472</span>    }</div>
</div>
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno">  473</span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;<a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a7d9ffc24f40f1824e0973e880c48d474" title="if NestedGridSearch was not configured before this call, it is default initialized ti the range[-1,...">::init</a>;</div>
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno">  474</span> </div>
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno">  475</span><span class="comment"></span> </div>
<div class="line"><a id="l00476" name="l00476"></a><span class="lineno">  476</span><span class="comment">    ///  Every call of the optimization member computes the</span></div>
<div class="line"><a id="l00477" name="l00477"></a><span class="lineno">  477</span><span class="comment">    ///  error landscape on the current grid. It picks the</span></div>
<div class="line"><a id="l00478" name="l00478"></a><span class="lineno">  478</span><span class="comment">    ///  best error value and zooms into the error landscape</span></div>
<div class="line"><a id="l00479" name="l00479"></a><span class="lineno">  479</span><span class="comment">    ///  by a factor of 2.</span></div>
<div class="foldopen" id="foldopen00480" data-start="{" data-end="}">
<div class="line"><a id="l00480" name="l00480"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#a6fdb96838b95142124d531921637f983">  480</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a6fdb96838b95142124d531921637f983">step</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction) {</div>
<div class="line"><a id="l00481" name="l00481"></a><span class="lineno">  481</span>        <span class="keywordtype">size_t</span> dimensions = <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>.size();</div>
<div class="line"><a id="l00482" name="l00482"></a><span class="lineno">  482</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a5a473052d0b073aadc27760d91a562cd" title="minimum parameter value to check">m_minimum</a>.size() == dimensions);</div>
<div class="line"><a id="l00483" name="l00483"></a><span class="lineno">  483</span>        <a class="code hl_define" href="_exception_8h.html#a42a6a50e4d06c00d60fbca5333f40768">SIZE_CHECK</a>(<a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#acf852729d6bcc8ed07b07b536c216b8f" title="maximum parameter value to check">m_maximum</a>.size() == dimensions);</div>
<div class="line"><a id="l00484" name="l00484"></a><span class="lineno">  484</span> </div>
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno">  485</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value = 1e99;</div>
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno">  486</span>        std::vector&lt;size_t&gt; index(dimensions,0);</div>
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno">  487</span> </div>
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno">  488</span>        RealVector point=<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point;</div>
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno">  489</span> </div>
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno">  490</span>        <span class="comment">// loop through the grid</span></div>
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno">  491</span>        <span class="keywordflow">while</span> (<span class="keyword">true</span>)</div>
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno">  492</span>        {</div>
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno">  493</span>            <span class="comment">// compute the grid point,</span></div>
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno">  494</span> </div>
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno">  495</span>            <span class="comment">// set the parameters</span></div>
<div class="line"><a id="l00496" name="l00496"></a><span class="lineno">  496</span>            <span class="keywordtype">bool</span> compute=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00497" name="l00497"></a><span class="lineno">  497</span>            <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> d = 0; d &lt; dimensions; d++)</div>
<div class="line"><a id="l00498" name="l00498"></a><span class="lineno">  498</span>            {</div>
<div class="line"><a id="l00499" name="l00499"></a><span class="lineno">  499</span>                point(d) += (index[d] - 2.0) * <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>[d];</div>
<div class="line"><a id="l00500" name="l00500"></a><span class="lineno">  500</span>                <span class="keywordflow">if</span> (point(d) &lt; <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a5a473052d0b073aadc27760d91a562cd" title="minimum parameter value to check">m_minimum</a>[d] || point(d) &gt; <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#acf852729d6bcc8ed07b07b536c216b8f" title="maximum parameter value to check">m_maximum</a>[d])</div>
<div class="line"><a id="l00501" name="l00501"></a><span class="lineno">  501</span>                {</div>
<div class="line"><a id="l00502" name="l00502"></a><span class="lineno">  502</span>                    compute = <span class="keyword">false</span>;</div>
<div class="line"><a id="l00503" name="l00503"></a><span class="lineno">  503</span>                    <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00504" name="l00504"></a><span class="lineno">  504</span>                }</div>
<div class="line"><a id="l00505" name="l00505"></a><span class="lineno">  505</span>            }</div>
<div class="line"><a id="l00506" name="l00506"></a><span class="lineno">  506</span> </div>
<div class="line"><a id="l00507" name="l00507"></a><span class="lineno">  507</span>            <span class="comment">// evaluate the grid point</span></div>
<div class="line"><a id="l00508" name="l00508"></a><span class="lineno">  508</span>            <span class="keywordflow">if</span> (compute &amp;&amp; objectiveFunction.isFeasible(point))</div>
<div class="line"><a id="l00509" name="l00509"></a><span class="lineno">  509</span>            {</div>
<div class="line"><a id="l00510" name="l00510"></a><span class="lineno">  510</span>                <span class="keywordtype">double</span> error = objectiveFunction.eval(point);</div>
<div class="line"><a id="l00511" name="l00511"></a><span class="lineno">  511</span> </div>
<div class="line"><a id="l00512" name="l00512"></a><span class="lineno">  512</span>                <span class="comment">// remember the best solution</span></div>
<div class="line"><a id="l00513" name="l00513"></a><span class="lineno">  513</span>                <span class="keywordflow">if</span> (error &lt; <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value)</div>
<div class="line"><a id="l00514" name="l00514"></a><span class="lineno">  514</span>                {</div>
<div class="line"><a id="l00515" name="l00515"></a><span class="lineno">  515</span>                    <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value = error;</div>
<div class="line"><a id="l00516" name="l00516"></a><span class="lineno">  516</span>                    <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point=point;</div>
<div class="line"><a id="l00517" name="l00517"></a><span class="lineno">  517</span>                }</div>
<div class="line"><a id="l00518" name="l00518"></a><span class="lineno">  518</span>            }</div>
<div class="line"><a id="l00519" name="l00519"></a><span class="lineno">  519</span>            <span class="comment">// move to the next grid point</span></div>
<div class="line"><a id="l00520" name="l00520"></a><span class="lineno">  520</span>            <span class="keywordtype">size_t</span> d = 0;</div>
<div class="line"><a id="l00521" name="l00521"></a><span class="lineno">  521</span>            <span class="keywordflow">for</span> (; d &lt; dimensions; d++)</div>
<div class="line"><a id="l00522" name="l00522"></a><span class="lineno">  522</span>            {</div>
<div class="line"><a id="l00523" name="l00523"></a><span class="lineno">  523</span>                index[d]++;</div>
<div class="line"><a id="l00524" name="l00524"></a><span class="lineno">  524</span>                <span class="keywordflow">if</span> (index[d] &lt;= 4) <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00525" name="l00525"></a><span class="lineno">  525</span>                index[d] = 0;</div>
<div class="line"><a id="l00526" name="l00526"></a><span class="lineno">  526</span>            }</div>
<div class="line"><a id="l00527" name="l00527"></a><span class="lineno">  527</span>            <span class="keywordflow">if</span> (d == dimensions) <span class="keywordflow">break</span>;</div>
<div class="line"><a id="l00528" name="l00528"></a><span class="lineno">  528</span>        }</div>
<div class="line"><a id="l00529" name="l00529"></a><span class="lineno">  529</span>        <span class="comment">// decrease the step sizes</span></div>
<div class="line"><a id="l00530" name="l00530"></a><span class="lineno">  530</span>        <span class="keywordflow">for</span>(<span class="keywordtype">double</span>&amp; <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a6fdb96838b95142124d531921637f983">step</a>: <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>)</div>
<div class="line"><a id="l00531" name="l00531"></a><span class="lineno">  531</span>            <a class="code hl_function" href="classshark_1_1_nested_grid_search.html#a6fdb96838b95142124d531921637f983">step</a> *= 0.5;</div>
<div class="line"><a id="l00532" name="l00532"></a><span class="lineno">  532</span>    }</div>
</div>
<div class="line"><a id="l00533" name="l00533"></a><span class="lineno">  533</span> </div>
<div class="line"><a id="l00534" name="l00534"></a><span class="lineno">  534</span><span class="keyword">protected</span>:<span class="comment"></span></div>
<div class="line"><a id="l00535" name="l00535"></a><span class="lineno">  535</span><span class="comment">    ///  minimum parameter value to check</span></div>
<div class="line"><a id="l00536" name="l00536"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#a5a473052d0b073aadc27760d91a562cd">  536</a></span><span class="comment"></span>    std::vector&lt;double&gt; <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a5a473052d0b073aadc27760d91a562cd" title="minimum parameter value to check">m_minimum</a>;</div>
<div class="line"><a id="l00537" name="l00537"></a><span class="lineno">  537</span><span class="comment"></span> </div>
<div class="line"><a id="l00538" name="l00538"></a><span class="lineno">  538</span><span class="comment">    ///  maximum parameter value to check</span></div>
<div class="line"><a id="l00539" name="l00539"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#acf852729d6bcc8ed07b07b536c216b8f">  539</a></span><span class="comment"></span>    std::vector&lt;double&gt; <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#acf852729d6bcc8ed07b07b536c216b8f" title="maximum parameter value to check">m_maximum</a>;</div>
<div class="line"><a id="l00540" name="l00540"></a><span class="lineno">  540</span><span class="comment"></span> </div>
<div class="line"><a id="l00541" name="l00541"></a><span class="lineno">  541</span><span class="comment">    ///  current step size for every parameter</span></div>
<div class="line"><a id="l00542" name="l00542"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2">  542</a></span><span class="comment"></span>    std::vector&lt;double&gt; <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#a715397003da7e1d62adf9b1abde79da2" title="current step size for every parameter">m_stepsize</a>;</div>
<div class="line"><a id="l00543" name="l00543"></a><span class="lineno">  543</span> </div>
<div class="line"><a id="l00544" name="l00544"></a><span class="lineno"><a class="line" href="classshark_1_1_nested_grid_search.html#aba7c13a5f426918e4e64760d7e5b3624">  544</a></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_nested_grid_search.html#aba7c13a5f426918e4e64760d7e5b3624">m_configured</a>;</div>
<div class="line"><a id="l00545" name="l00545"></a><span class="lineno">  545</span>};</div>
</div>
<div class="line"><a id="l00546" name="l00546"></a><span class="lineno">  546</span> </div>
<div class="line"><a id="l00547" name="l00547"></a><span class="lineno">  547</span><span class="comment"></span> </div>
<div class="line"><a id="l00548" name="l00548"></a><span class="lineno">  548</span><span class="comment">/// </span></div>
<div class="line"><a id="l00549" name="l00549"></a><span class="lineno">  549</span><span class="comment">///  \brief Optimize by trying out predefined configurations</span></div>
<div class="line"><a id="l00550" name="l00550"></a><span class="lineno">  550</span><span class="comment">/// </span></div>
<div class="line"><a id="l00551" name="l00551"></a><span class="lineno">  551</span><span class="comment">///  \par</span></div>
<div class="line"><a id="l00552" name="l00552"></a><span class="lineno">  552</span><span class="comment">///  The PointSearch class is similair to the GridSearch class</span></div>
<div class="line"><a id="l00553" name="l00553"></a><span class="lineno">  553</span><span class="comment">///  by the property that it optimizes a model in a single pass</span></div>
<div class="line"><a id="l00554" name="l00554"></a><span class="lineno">  554</span><span class="comment">///  just trying out a predefined number of parameter configurations.</span></div>
<div class="line"><a id="l00555" name="l00555"></a><span class="lineno">  555</span><span class="comment">///  The main difference is that every parameter configuration has</span></div>
<div class="line"><a id="l00556" name="l00556"></a><span class="lineno">  556</span><span class="comment">///  to be explicitly defined. It is not possible to define a set</span></div>
<div class="line"><a id="l00557" name="l00557"></a><span class="lineno">  557</span><span class="comment">///  of values for every axis; see GridSearch for this purpose.</span></div>
<div class="line"><a id="l00558" name="l00558"></a><span class="lineno">  558</span><span class="comment">///  Thus, the PointSearch class allows for more flexibility.</span></div>
<div class="line"><a id="l00559" name="l00559"></a><span class="lineno">  559</span><span class="comment">/// </span></div>
<div class="line"><a id="l00560" name="l00560"></a><span class="lineno">  560</span><span class="comment">///  If no configure method is called, this class just samples random points.</span></div>
<div class="line"><a id="l00561" name="l00561"></a><span class="lineno">  561</span><span class="comment">///  They are uniformly distributed in [-1,1].</span></div>
<div class="line"><a id="l00562" name="l00562"></a><span class="lineno">  562</span><span class="comment">///  parameters^2 points but minimum 20 are sampled in this case.</span></div>
<div class="line"><a id="l00563" name="l00563"></a><span class="lineno">  563</span><span class="comment">/// </span></div>
<div class="foldopen" id="foldopen00564" data-start="{" data-end="};">
<div class="line"><a id="l00564" name="l00564"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html">  564</a></span><span class="comment"></span><span class="keyword">class </span><a class="code hl_class" href="classshark_1_1_point_search.html" title="Optimize by trying out predefined configurations.">PointSearch</a> : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;</div>
<div class="line"><a id="l00565" name="l00565"></a><span class="lineno">  565</span>{</div>
<div class="line"><a id="l00566" name="l00566"></a><span class="lineno">  566</span><span class="keyword">public</span>:<span class="comment"></span></div>
<div class="line"><a id="l00567" name="l00567"></a><span class="lineno">  567</span><span class="comment">    ///  Constructor</span></div>
<div class="foldopen" id="foldopen00568" data-start="{" data-end="}">
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#a8ae721d0d9d38babed22d1c81525c27a">  568</a></span><span class="comment"></span>    <a class="code hl_function" href="classshark_1_1_point_search.html#a8ae721d0d9d38babed22d1c81525c27a" title="Constructor.">PointSearch</a>() {</div>
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno">  569</span>        <a class="code hl_variable" href="classshark_1_1_point_search.html#ae00113b568af68edabb8e2637a4b12ac" title="verbosity level">m_configured</a>=<span class="keyword">false</span>;</div>
<div class="line"><a id="l00570" name="l00570"></a><span class="lineno">  570</span>    }</div>
</div>
<div class="line"><a id="l00571" name="l00571"></a><span class="lineno">  571</span><span class="comment"></span> </div>
<div class="line"><a id="l00572" name="l00572"></a><span class="lineno">  572</span><span class="comment">    /// \brief From INameable: return the class name.</span></div>
<div class="foldopen" id="foldopen00573" data-start="{" data-end="}">
<div class="line"><a id="l00573" name="l00573"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#a276d5caed734133125aa479772616f19">  573</a></span><span class="comment"></span>    std::string <a class="code hl_function" href="classshark_1_1_point_search.html#a276d5caed734133125aa479772616f19" title="From INameable: return the class name.">name</a>()<span class="keyword"> const</span></div>
<div class="line"><a id="l00574" name="l00574"></a><span class="lineno">  574</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00575" name="l00575"></a><span class="lineno">  575</span>        <span class="keywordflow">return</span> <span class="stringliteral">&quot;PointSearch&quot;</span>;</div>
<div class="line"><a id="l00576" name="l00576"></a><span class="lineno">  576</span>    }</div>
</div>
<div class="line"><a id="l00577" name="l00577"></a><span class="lineno">  577</span><span class="comment"></span> </div>
<div class="line"><a id="l00578" name="l00578"></a><span class="lineno">  578</span><span class="comment">    ///  Initialization of the search points.</span></div>
<div class="line"><a id="l00579" name="l00579"></a><span class="lineno">  579</span><span class="comment">    ///  \param  points  array of points to evaluate</span></div>
<div class="foldopen" id="foldopen00580" data-start="{" data-end="}">
<div class="line"><a id="l00580" name="l00580"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#a17f57ad1f208eb3d036b083fafd0180c">  580</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_point_search.html#a17f57ad1f208eb3d036b083fafd0180c">configure</a>(<span class="keyword">const</span> std::vector&lt;RealVector&gt;&amp; points) {</div>
<div class="line"><a id="l00581" name="l00581"></a><span class="lineno">  581</span>        <a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>=points;</div>
<div class="line"><a id="l00582" name="l00582"></a><span class="lineno">  582</span>        <a class="code hl_variable" href="classshark_1_1_point_search.html#ae00113b568af68edabb8e2637a4b12ac" title="verbosity level">m_configured</a>=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00583" name="l00583"></a><span class="lineno">  583</span>    }</div>
</div>
<div class="line"><a id="l00584" name="l00584"></a><span class="lineno">  584</span><span class="comment"></span> </div>
<div class="line"><a id="l00585" name="l00585"></a><span class="lineno">  585</span><span class="comment">    /// samples random points in the range [min,max]^parameters</span></div>
<div class="foldopen" id="foldopen00586" data-start="{" data-end="}">
<div class="line"><a id="l00586" name="l00586"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#abff565fde868c310e536650d19b0cc6f">  586</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_point_search.html#abff565fde868c310e536650d19b0cc6f" title="samples random points in the range [min,max]^parameters">configure</a>(<span class="keywordtype">size_t</span> parameters,<span class="keywordtype">size_t</span> samples, <span class="keywordtype">double</span> min,<span class="keywordtype">double</span> max) {</div>
<div class="line"><a id="l00587" name="l00587"></a><span class="lineno">  587</span>        <a class="code hl_define" href="_exception_8h.html#abd848215f138fc44f696aecb3e417e6c">RANGE_CHECK</a>(min&lt;=max);</div>
<div class="line"><a id="l00588" name="l00588"></a><span class="lineno">  588</span>        <a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>.resize(samples);</div>
<div class="line"><a id="l00589" name="l00589"></a><span class="lineno">  589</span>        <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> sample=0; sample!=samples; ++sample)</div>
<div class="line"><a id="l00590" name="l00590"></a><span class="lineno">  590</span>        {</div>
<div class="line"><a id="l00591" name="l00591"></a><span class="lineno">  591</span>            <a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>[sample].resize(parameters);</div>
<div class="line"><a id="l00592" name="l00592"></a><span class="lineno">  592</span>            <span class="keywordflow">for</span>(<span class="keywordtype">size_t</span> param=0; param!=parameters; ++param)</div>
<div class="line"><a id="l00593" name="l00593"></a><span class="lineno">  593</span>            {</div>
<div class="line"><a id="l00594" name="l00594"></a><span class="lineno">  594</span>                <a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>[sample](param)=<a class="code hl_function" href="namespaceshark_1_1random.html#a18f302ea18f70835c59935973ba8ea84" title="Draws a number uniformly in [lower,upper] by drawing random numbers from rng.">random::uni</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, min,max);</div>
<div class="line"><a id="l00595" name="l00595"></a><span class="lineno">  595</span>            }</div>
<div class="line"><a id="l00596" name="l00596"></a><span class="lineno">  596</span>        }</div>
<div class="line"><a id="l00597" name="l00597"></a><span class="lineno">  597</span>        <a class="code hl_variable" href="classshark_1_1_point_search.html#ae00113b568af68edabb8e2637a4b12ac" title="verbosity level">m_configured</a>=<span class="keyword">true</span>;</div>
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno">  598</span>    }</div>
</div>
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno">  599</span> </div>
<div class="foldopen" id="foldopen00600" data-start="{" data-end="}">
<div class="line"><a id="l00600" name="l00600"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#afc65c684eb51d11d522c324ea6ebce6a">  600</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_point_search.html#afc65c684eb51d11d522c324ea6ebce6a" title="Read the component from the supplied archive.">read</a>( <a class="code hl_typedef" href="namespaceshark.html#ada68729491840669e47c8ad42282424f" title="Type of an archive to read from.">InArchive</a> &amp; archive )</div>
<div class="line"><a id="l00601" name="l00601"></a><span class="lineno">  601</span>    {</div>
<div class="line"><a id="l00602" name="l00602"></a><span class="lineno">  602</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>;</div>
<div class="line"><a id="l00603" name="l00603"></a><span class="lineno">  603</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_point_search.html#ae00113b568af68edabb8e2637a4b12ac" title="verbosity level">m_configured</a>;</div>
<div class="line"><a id="l00604" name="l00604"></a><span class="lineno">  604</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point;</div>
<div class="line"><a id="l00605" name="l00605"></a><span class="lineno">  605</span>        archive&gt;&gt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value;</div>
<div class="line"><a id="l00606" name="l00606"></a><span class="lineno">  606</span>    }</div>
</div>
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno">  607</span> </div>
<div class="foldopen" id="foldopen00608" data-start="{" data-end="}">
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#a6fa53e94f0eeceabbc5bbf868b5ed77b">  608</a></span>    <span class="keyword">virtual</span> <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_point_search.html#a6fa53e94f0eeceabbc5bbf868b5ed77b" title="Write the component to the supplied archive.">write</a>( <a class="code hl_typedef" href="namespaceshark.html#af4f8eb8e9618f5236b71bbcb12b8a524" title="Type of an archive to write to.">OutArchive</a> &amp; archive )<span class="keyword"> const</span></div>
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno">  609</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno">  610</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>;</div>
<div class="line"><a id="l00611" name="l00611"></a><span class="lineno">  611</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_point_search.html#ae00113b568af68edabb8e2637a4b12ac" title="verbosity level">m_configured</a>;</div>
<div class="line"><a id="l00612" name="l00612"></a><span class="lineno">  612</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point;</div>
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno">  613</span>        archive&lt;&lt;<a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value;</div>
<div class="line"><a id="l00614" name="l00614"></a><span class="lineno">  614</span>    }</div>
</div>
<div class="line"><a id="l00615" name="l00615"></a><span class="lineno">  615</span><span class="comment"></span> </div>
<div class="line"><a id="l00616" name="l00616"></a><span class="lineno">  616</span><span class="comment">    ///  If the class wasn&#39;t configured before, this method samples random uniform distributed points in [-1,1]^n.</span></div>
<div class="foldopen" id="foldopen00617" data-start="{" data-end="}">
<div class="line"><a id="l00617" name="l00617"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#a526bcfc4e0bcfd6bd4d4c937347f5ad6">  617</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_point_search.html#a526bcfc4e0bcfd6bd4d4c937347f5ad6" title="If the class wasn&#39;t configured before, this method samples random uniform distributed points in [-1,...">init</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction, <a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#a85f0d04fdfb094dba4dc80b1fb5e3adb">SearchPointType</a> <span class="keyword">const</span>&amp; startingPoint) {</div>
<div class="line"><a id="l00618" name="l00618"></a><span class="lineno">  618</span>        <a class="code hl_function" href="classshark_1_1_abstract_optimizer.html#ae7a23300641448c761b6aa0305b7ef66" title="Convenience function that checks whether the features of the supplied objective function match with t...">checkFeatures</a>(objectiveFunction);</div>
<div class="line"><a id="l00619" name="l00619"></a><span class="lineno">  619</span> </div>
<div class="line"><a id="l00620" name="l00620"></a><span class="lineno">  620</span>        <span class="keywordflow">if</span>(!<a class="code hl_variable" href="classshark_1_1_point_search.html#ae00113b568af68edabb8e2637a4b12ac" title="verbosity level">m_configured</a>)</div>
<div class="line"><a id="l00621" name="l00621"></a><span class="lineno">  621</span>        {</div>
<div class="line"><a id="l00622" name="l00622"></a><span class="lineno">  622</span>            <span class="keywordtype">size_t</span> parameters=startingPoint.size();</div>
<div class="line"><a id="l00623" name="l00623"></a><span class="lineno">  623</span>            <span class="keywordtype">size_t</span> samples=std::min(<a class="code hl_function" href="group__shark__globals.html#gae1f82613484173e9fe1a07960dabff63" title="Calculates x^2.">sqr</a>(parameters),(<span class="keywordtype">size_t</span>)20);</div>
<div class="line"><a id="l00624" name="l00624"></a><span class="lineno">  624</span>            <a class="code hl_function" href="classshark_1_1_point_search.html#a17f57ad1f208eb3d036b083fafd0180c">configure</a>(parameters,samples,-1,1);</div>
<div class="line"><a id="l00625" name="l00625"></a><span class="lineno">  625</span>        }</div>
<div class="line"><a id="l00626" name="l00626"></a><span class="lineno">  626</span>    }</div>
</div>
<div class="line"><a id="l00627" name="l00627"></a><span class="lineno">  627</span>    <span class="keyword">using </span><a class="code hl_class" href="classshark_1_1_abstract_single_objective_optimizer.html" title="Base class for all single objective optimizer.">AbstractSingleObjectiveOptimizer</a>&lt;RealVector &gt;<a class="code hl_function" href="classshark_1_1_point_search.html#a526bcfc4e0bcfd6bd4d4c937347f5ad6" title="If the class wasn&#39;t configured before, this method samples random uniform distributed points in [-1,...">::init</a>;<span class="comment"></span></div>
<div class="line"><a id="l00628" name="l00628"></a><span class="lineno">  628</span><span class="comment">    ///  Please note that for the point search optimizer it does</span></div>
<div class="line"><a id="l00629" name="l00629"></a><span class="lineno">  629</span><span class="comment">    ///  not make sense to call step more than once, as the</span></div>
<div class="line"><a id="l00630" name="l00630"></a><span class="lineno">  630</span><span class="comment">    ///  solution does not improve iteratively.</span></div>
<div class="foldopen" id="foldopen00631" data-start="{" data-end="}">
<div class="line"><a id="l00631" name="l00631"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#aff93d5e557f30ffe2dd29556c84881c5">  631</a></span><span class="comment"></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_point_search.html#aff93d5e557f30ffe2dd29556c84881c5">step</a>(<a class="code hl_typedef" href="classshark_1_1_abstract_single_objective_optimizer.html#aa4c05609c54d7ebc99d099e7dd6e228f">ObjectiveFunctionType</a> <span class="keyword">const</span>&amp; objectiveFunction) {</div>
<div class="line"><a id="l00632" name="l00632"></a><span class="lineno">  632</span>        <span class="keywordtype">size_t</span> numPoints = <a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>.size();</div>
<div class="line"><a id="l00633" name="l00633"></a><span class="lineno">  633</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value = 1e100;</div>
<div class="line"><a id="l00634" name="l00634"></a><span class="lineno">  634</span>        <span class="keywordtype">size_t</span> bestIndex=0;</div>
<div class="line"><a id="l00635" name="l00635"></a><span class="lineno">  635</span> </div>
<div class="line"><a id="l00636" name="l00636"></a><span class="lineno">  636</span>        <span class="comment">// loop through all points</span></div>
<div class="line"><a id="l00637" name="l00637"></a><span class="lineno">  637</span>        <span class="keywordflow">for</span> (<span class="keywordtype">size_t</span> point = 0; point &lt; numPoints; point++)</div>
<div class="line"><a id="l00638" name="l00638"></a><span class="lineno">  638</span>        {</div>
<div class="line"><a id="l00639" name="l00639"></a><span class="lineno">  639</span>            <span class="comment">// evaluate the model</span></div>
<div class="line"><a id="l00640" name="l00640"></a><span class="lineno">  640</span>            <span class="keywordflow">if</span> (objectiveFunction.isFeasible(<a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>[point]))</div>
<div class="line"><a id="l00641" name="l00641"></a><span class="lineno">  641</span>            {</div>
<div class="line"><a id="l00642" name="l00642"></a><span class="lineno">  642</span>                <span class="keywordtype">double</span> error = objectiveFunction.eval(<a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>[point]);</div>
<div class="line"><a id="l00643" name="l00643"></a><span class="lineno">  643</span>                <span class="keywordflow">if</span> (error &lt; <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value)</div>
<div class="line"><a id="l00644" name="l00644"></a><span class="lineno">  644</span>                {</div>
<div class="line"><a id="l00645" name="l00645"></a><span class="lineno">  645</span>                    <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.value = error;</div>
<div class="line"><a id="l00646" name="l00646"></a><span class="lineno">  646</span>                    bestIndex=point;</div>
<div class="line"><a id="l00647" name="l00647"></a><span class="lineno">  647</span>                }</div>
<div class="line"><a id="l00648" name="l00648"></a><span class="lineno">  648</span>            }</div>
<div class="line"><a id="l00649" name="l00649"></a><span class="lineno">  649</span>        }</div>
<div class="line"><a id="l00650" name="l00650"></a><span class="lineno">  650</span>        <a class="code hl_variable" href="classshark_1_1_abstract_single_objective_optimizer.html#a4740a0f8e9d5c7d99cf0dd0c3ee0e8a0" title="Current solution of the optimizer.">m_best</a>.point=<a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>[bestIndex];</div>
<div class="line"><a id="l00651" name="l00651"></a><span class="lineno">  651</span>    }</div>
</div>
<div class="line"><a id="l00652" name="l00652"></a><span class="lineno">  652</span> </div>
<div class="line"><a id="l00653" name="l00653"></a><span class="lineno">  653</span><span class="keyword">protected</span>:<span class="comment"></span></div>
<div class="line"><a id="l00654" name="l00654"></a><span class="lineno">  654</span><span class="comment">    ///  The array holds one parameter configuration in every column.</span></div>
<div class="line"><a id="l00655" name="l00655"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d">  655</a></span><span class="comment"></span>    std::vector&lt;RealVector&gt; <a class="code hl_variable" href="classshark_1_1_point_search.html#a313d5465872857944cce410b2a65538d" title="The array holds one parameter configuration in every column.">m_points</a>;</div>
<div class="line"><a id="l00656" name="l00656"></a><span class="lineno">  656</span><span class="comment"></span> </div>
<div class="line"><a id="l00657" name="l00657"></a><span class="lineno">  657</span><span class="comment">    ///  verbosity level</span></div>
<div class="line"><a id="l00658" name="l00658"></a><span class="lineno"><a class="line" href="classshark_1_1_point_search.html#ae00113b568af68edabb8e2637a4b12ac">  658</a></span><span class="comment"></span>    <span class="keywordtype">bool</span> <a class="code hl_variable" href="classshark_1_1_point_search.html#ae00113b568af68edabb8e2637a4b12ac" title="verbosity level">m_configured</a>;</div>
<div class="line"><a id="l00659" name="l00659"></a><span class="lineno">  659</span>};</div>
</div>
<div class="line"><a id="l00660" name="l00660"></a><span class="lineno">  660</span> </div>
<div class="line"><a id="l00661" name="l00661"></a><span class="lineno">  661</span> </div>
<div class="line"><a id="l00662" name="l00662"></a><span class="lineno">  662</span>}</div>
<div class="line"><a id="l00663" name="l00663"></a><span class="lineno">  663</span><span class="preprocessor">#endif</span></div>
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